The present invention is a method for analyzing an unknown material using a multivariate analytical technique such as spectroscopy, or a combination of a multivariate analytical technique and inspections. Such inspections are physical or chemical property measurements that can be made cheaply and easily on the bulk material, and include but are not limited to API or specific gravity and viscosity. The unknown material is analyzed by comparing its multivariate analytical data (e.g. spectrum) or its multivariate analytical data and inspections to a database containing multivariate analytical data or multivariate analytical data and inspection data for reference materials of the same type. The comparison is done so as to calculate a blend of a subset of the reference materials that matches the containing multivariate analytical data or containing multivariate analytical data and inspections of the unknown. The calculated blend of the reference materials is then used to predict additional chemical, physical or performance properties of the unknown using measured chemical, physical and performance properties of the reference materials and known blending relationships.
Within the petrochemical industry, there are many instances where a very detailed analyses of a process feed or product is needed for the purpose of making business decisions, planning, controlling and optimizing operations, and certifying products. Herein below, such a detailed analysis will be referred to as an assay, a crude assay being one example thereof. The methodology used in the detailed analysis may be costly and time consuming to perform, and may not be amenable to real time analysis. It is desirable to have a surrogate methodology that can provide the information of the detailed analysis inexpensively and in a timely fashion. The present invention is one such surrogate methodology.
Infrared spectroscopy, and in particular near-infrared spectroscopy, is widely used for the quantitative analysis of petrochemicals. For most applications, linear regression models are developed that relate the measured spectrum to the chemical, physical and performance properties of the material. Chemical properties include but are not limited to elemental and molecular compositions. Physical properties include but are not limited to density, viscosity, and cold flow properties such as pour, cloud or freeze point. Performance properties include but are not limited to octane and cetane numbers. While such linear regression models have been successfully used for many petrochemical applications, they are of limited utility for the detailed analysis of process feeds and products. The detailed analysis (assay) may involve hundreds of chemical, physical and performance parameters, thereby requiring the development and maintenance of an unmanageably large number of regression models. Further, many of the properties of interest may be complex, nonlinear functions of composition that are not readily predicted using linear regression models. Finally, the detailed analysis (assay) may include composition and property data for subfractions of the whole sample that are not readily predicted using linear regression models based on spectra of the whole sample. The current invention avoids these limitations by using a novel algorithmic approach to represent an unknown material as a blend of known reference materials. The current invention can readily predict large numbers of chemical, physical and performance properties of a material, can predict nonlinear properties providing nonlinear blending rules are known, and can predict chemical, physical and performance properties of subfractions of a material providing such properties were measured on similar subfractions of the reference materials and providing that blending relationships for the properties are known.
Alternative approaches that do not involve linear regression have been applied to spectroscopic data in an attempt to predict chemical, physical and performance properties of petrochemicals. For example, non-linear post-processing methods and neural networks have been employed to improve predictions for properties that are nonlinear functions of composition. Application of these analyses might address non-linearity, but they would only add to the complexity of the unmanageably large number of models needed for prediction of the detailed analysis (assay). Topology based approaches have been applied to spectral data so as to identify reference materials that are sufficiently similar to the material being analyzed to allow properties to be inferred. However, the topology approach requires a much denser database than the current invention to ensure that there are sufficiently similar references to any sample being analyzed. For detailed analyses (assays), the cost of producing a sufficiently dense database to utilize the topological approach is prohibitive. None of the alternative approaches have been shown to be reliably capable of predicting properties of sub-fractions of a sample based on spectra of the whole sample.
While the preferred embodiment of the present invention utilizes extended mid-infrared spectroscopy (7000xe2x88x92400 cmxe2x88x921), similar results could potentially be obtained using other multivariate analytical techniques. Such multivariate analytical techniques include other forms of spectroscopy including but not limited to near-infrared spectroscopy (12500xe2x88x927000 cmxe2x88x921), UV/visible spectroscopy (200-800 nm), fluorescence and NMR spectroscopy. Similar analyses could also potentially be done using data derived multivariate analytical techniques such as simulated gas chromatographic distillation (GCD) and mass spectrometry or from combined multivariate analytical techniques such as GC/MS. In this context, the use of the word spectra herein below includes any vector or array of analytical data generated by a multivariate analytical measurement such as spectroscopy, chromatography or spectrometry or their combinations.
The present invention is applicable to the prediction of chemical, physical and performance properties of crude oils. Both properties of whole crude, and of any distillate cut of the crude can be predicted. The present invention is also applicable to petrochemical process and product streams. The reference materials used in the analysis and the unknowns that are analyzed can be process feeds, products or both. For example, the reference materials can be gas oil feeds to a catalytic cracking unit for which detailed molecular composition analyses have been performed. The present invention can be used to predict the molecular compositions of unknown gas oils. The present invention is also applicable to the prediction of extraction response data for waxy distillate feeds to lube extraction and dewaxing processes. The extraction response data includes but is not limited to raffinate and dewaxed raffinate yield, raffinate and dewaxed raffinate viscosity and viscosity index, raffinate and dewaxed raffinate saturates content, and raffinate and dewaxed raffinate refractive index as a function of extraction and dewaxing conditions. The reference materials are waxy distillate feed samples for which extraction and dewaxing data was measured. The present invention is used to predict extraction and dewaxing data for unknown waxy distillate feeds.
In the petrochemical industry, extremely detailed analyses of feed and product materials (assays) are often utilized for making business decisions, for planning, controlling and optimizing operations, and for certifying products. Chief among these analyses is the crude assay. When a crude oil is assayed, it is distilled in two steps. A method such as ASTM D2892 (see. Annual Book of ASTM Standards, Volumes 5.01-5.03, American Society for Testing and Materials, Philadelphia, Pa.) is used to isolate distillate cuts boiling below approximately 650xc2x0 F. (343xc2x0 C.). The residue from this distillation is further distilled using a method such as ASTM D5236 to produce distillate cuts covering the range from 650xc2x0 F. to approximately 1000-1054xc2x0 F. (343xc2x0 C. to 538-568xc2x0 C.) and a vacuum residue cut. At a minimum, cuts corresponding to typical products or unit feeds are typically isolated, including LPG (Initial Boiling Point to 68xc2x0 F.), LSR (68-155xc2x0 F.), naphtha (155-350xc2x0 F.), kerosene (350-500xc2x0 F.), diesel (500-650xc2x0 F.), vacuum gas oil (650xc2x0 F. to 1000-1054xc2x0 F.), and vacuum residue (1000-1054xc2x0 F.+). Each distillate cut is then analyzed for elemental, molecular, physical and/or performance properties. The specific analyses conducted depend on the typical disposition of the cut. Example analyses are shown in Table 1. The data derived from these analyses will typically be stored is in an electronic database where it can be mathematically manipulated to estimate crude qualities for any desired distillation range. For example, commercial crude assay libraries are available from Haverly Systems Inc., and HPI Consultants Inc., both of which provide tools for manipulating the data, as does Aspentech Inc. Assay data is published by Crude Quality Inc., by Shell Oil Company, and by Statoil. The property versus distillation temperature data is typically fit to smooth curves that can then be used to estimate the property for any desired distillation cut.
Depending on the intended use of the assay data, different organizations will employ different assay strategies. If more distillate cuts are taken covering smaller temperature ranges, the accuracy of the property versus temperature curves is improved. However, the volume of oil that needs to be distilled to provide adequate samples for reference analyses is increased, as is the number of required analyses. Thus, the cost and completion time of the assay is increased. For compositional and process modeling, extremely detailed analyses may be employed, as for example the HDHA method described by Jacob, Quann, Sanchez and Wells (Oil and Gas Journal, Jul. 6, 1998).
A detailed crude assay can take several weeks to months to complete. As a result, the assay data used for making business decisions, and for planning, controlling and optimizing operations is seldom from the cargoes currently being bought, sold or processed, but rather historical data for xe2x80x9crepresentativexe2x80x9d past cargoes. The assays do not account for variations between cargoes that can have a significant effect on operations. K. G. Waguespack (Hydrocarbon Processing, 77 (9), 1998 Feature Article) discusses the sources of oil quality variation, their effect on refinery operations, and the need for improved analytical technology for use in crude oil quality monitoring. Wagusepack lists sources of crude oil variability, both over time and during its transport life as: aging production reservoirs; changes in relative field production rates; mixing of crude in the gathering system; pipeline degradation vis-à-vis batch interfaces; contamination; and injection of significantly different quality streams into common specification crude streams. Such variations can cause significant changes in the value of the crude oil, and in the products that can be made from it.
Detailed analyses are conducted on many petrochemical feeds and products. R. J. Quann and S. B. Jaffe (Ind. Eng. Chem. Res. 1992, 31, 2483-2497) describe a Structured Oriented Lumping scheme for use in modeling petrochemical processes. The SOL scheme utilizes data collected via a combination of HPLC, field ionization mass spectrometry and gas chromatography/mass spectrometry (GC/MS) (Sullivan, R. F.; Bodluszynaski, M. M.; Fetzer, J. C.; Energy Fuels 1989, 3, 603-612). Jacob, et. al. (Jacob, S. M.; Quann, R. J.; Sanchez, E.; Wells, M. E.; Oil and Gas Journal 1998, 51-58) describe application of the SOL approach to various refining processes involved in lubricant manufacture. The analysis schemes used to generate the SOL data are complex and time consuming to apply. The current invention helps to maximize the utility of these SOL based process models for business decisions by providing a means of generating the SOL data rapidly, on minimal sample volumes.
Infrared and Raman spectroscopies have been employed for process analysis of a variety of petrochemical streams. G. M. Hieftje, D. E. Honigs and T. B. Hirschfeld (U.S. Pat. No. 4,800,279 Jan. 24, 1989) described the prediction of physical properties for simple hydrocarbon mixtures from near-infrared (NIR) spectra using multiple linear regression (MLR). D. A. Swinkels, P. M. Fredricks and P. R. Osborn applied FT-IR and Principal Components Regression (PCR) to the analysis of coals (U.S. Pat. No. 4,701,838 Oct. 20, 1987). J. M. Brown (U.S. Pat. No. 5,121,337 Jun. 9, 1992) describes a method for predicting property and composition data of samples using spectra and Constrained Principal Spectra Analysis (CPSA). R. Clarke describes a method for measuring properties of hydrocarbons using Raman spectroscopy (U.S. Pat. No. 5,139,334 Aug. 18, 1992). R. H. Clarke and D. Tang describe a method and mid-infrared apparatus for determining hydrocarbon fuel properties (U.S. Pat. No. 5,225,679 Jul. 6, 1993). D. C. Lambert and A. Martens (EP 2852521 and U.S. 5490085 Sep. 6, 1996) describe the prediction of octane number using NIR spectra and MLR, as does S. M. Maggard (U.S. Pat. No. 4,963,745 Oct. 16, 1990). Maggard also describes the estimation of paraffins, isoparaffins, aromatics, naphthenes and olefins in gasolines using NIR and MLR or Partial Least Squares (PLS) (U.S. Pat. No. 5,349,188 Sep. 20, 1994), the prediction of blend properties from the spectra of blend components using NIR and MLR (U.S. Pat. No. 5,223,714 Jun. 29, 193), and the prediction of oxygenates and oxygen content of gasolines using NIR spectra. S. Maggard and W. T. Welch discuss prediction of organic sulfur content for mid-distillate fuels using NIR spectra (U.S. Pat. No. 5,348,645 Sep. 20, 194). J. B. Cooper, M. B. Sumner; W. T. Welch and K. L Wise describe a method for measuring oxygen and oxygenate content of gasolines using Raman spectroscopy (U.S. Pat. No. 5,596,196 Feb. 21, 1997). R. R. Bledsoe, J. B. Cooper, M. B. Sumner; W. T. Welch, B. K. Wilt and K. L Wise describe a method of predicting octane number and Reid vapor pressure of gasolines using Raman spectroscopy (U.S. Pat. No. 5,892,228 Apr. 6, 1999). These methods typically involve linear models for individual properties, and are thus not necessarily useful for properties that are nonlinear functions of composition, nor for prediction of properties of subfractions of the sample being analyzed. While they can provide rapid analyses on minimal sample volumes, their application for detailed analyses would require the development and maintenance of an impracticably large number of models. In addition, many of these NIR methods operate in spectral regions where crude oil is essentially opaque. Raman methods are typically not applicable to crude oils or other heavy hydrocarbons due to interferences from fluorescence. None of these methods employs a combination of infrared spectra and inspections.
A. Espinosa, A. Martens, G. Ventron, D. C. Lambert and A. Pasquier (EP 305090 and U.S. Pat. No. 5,475,612 Dec. 12, 1995) describe predicting physical properties of blends from near-infrared spectra of blend components using MLR. Products and ratios of absorbances were included in an attempt to predict nonlinear properties such as RON. A. Espinosa, D. C. Lambert, A. Martens and G. Ventron (EP 304232 and U.S. Pat. No. 5,452,232 Apr. 25, 1990) describe a method for predicting properties of process products from spectra of process feeds using NIR and MLR. Products and ratios of absorbances were again used to handle nonlinear properties. B. N. Perry and J. M. Brown describe a method for improving the prediction of nonlinear properties by post-processing results from linear models (U.S. Pat. No. 5,641,962 Jun. 24, 1997). J. M. Tolchard and A. Boyd (WO9417391) describe the use of NIR and neural networks for the prediction of hydrocarbon physical properties. While these methods could potentially be use to predict properties that have nonlinear relationships to composition, all would require that separate models be built for each property to be predicted. In addition, none of these methods uses spectra in combination with inspections.
R. DiFoggio, M. Sadhukhan and M. Ranc (U.S. Pat. No. 5,360,972 Nov. 1, 1994) describe a method for estimating physical properties of a material using a combination of infrared data and data indicative of trace level compounds. DiFoggio et. al. do not teach the use of infrared and inspection data, and their method would require separate models to be built for each property to be estimated.
B. Descales, D. Lambert, J. LLinas, A. Martens, S. Osta, M. Sanchez and S. Bages (U.S. Pat. No. 6,070,128 May 30, 2000) describe a topology based method for determining properties from NIR spectra. Their method calculates an Euclidean distance between the spectrum of the sample being analyzed and all of the reference spectra in the database. Reference samples whose spectra fall within a predetermined distance of the unknown spectra are selected, and the properties of the unknown are calculated as the average of the properties of the selected references. Alternatively, the spectrum of the unknown can be fit as a linear combination of the selected references, and the properties of the unknown calculated as the weighted combination of the reference sample properties. Nonlinear properties are handled through blending factors. If there are insufficient references within the predetermined distance of the unknown, the method provides a means of densifying the database to interpolate between the reference samples. While the method of Descales, et. al. can be used to analyze the unknown as if it were a blend of the reference samples, the blend components are limited to those samples who have spectra nearly identical to the spectrum of the unknown, i.e. the nearest neighbors in the spectral space. In addition, Descales, et. al. do not teach the combination of infrared and inspection data.
Other methodologies have been employed for detailed analyses of hydrocarbons. T. R. Ashe, R. W. Kapala and G. Roussis (U.S. Pat. No. 5,699,270 Dec. 16, 1997) employed PLS models of GC/MS data to predict chemical, performance, perceptual and physical properties of feed and product streams from various steps in lubricating oil manufacturing. T. R. Ashe, S. G. Roussis, J. W. Fedora, G. Felshy and W. P. Fitzgerald (U.S. Pat. No. 5,699,269 Dec. 16 ,1997) used PLS models of OGC/MS data to predict physical and chemical properties of crude oils. Both method employed separate models for each property predicted.
I. H. Cho, J. G. Choi and H. I. Chung (WO 00/39561) described an apparatus that combined a distillation unit and a spectrometer for analysis of crude oils. Separate chemometric models were employed for each property for each distillate cut.
K. Hidajat and S. M. Chong claim to measure total boiling point and density of crude oils from NIR spectra (J. Near Infrared Spectroscopy 8, 53-59 (2000)). Neither other whole crude properties, nor properties of distillate cuts were predicted.
The present invention is a method for determining a property of an unknown material. The invention includes the steps of determining the multivariate analytical data of the unknown material, fitting the multivariate analytical data to a linear combination of known multivariate analytical data in a database, wherein the database includes multivariate analytical data of reference materials whose assay properties are known, and determining the property of the unknown material from the assay properties of the reference materials.
In a preferred embodiment, the method includes the step of eliminating signals from the multivariate analytical data not relating to the molecular constituents. The step of eliminating signals may be performed by orthogonalizing the multivariate analytical data of the unknown and reference materials to examples of the signals to be eliminated.
In another preferred embodiment the method further includes the step of augmenting the multivariate analytical data with inspection data to form augmented data such that the augmented data of the unknown material is fit to a linear combination of multivariate analytical data augmented with inspection data of the known reference materials. The inspection data may be, but is not limited to, specific gravity or viscosity.